Multilevel Categorization of Bengali News Headlines using Bidirectional Gated Recurrent Unit

2021 ◽  
Author(s):  
Ettilla Mohiuddin ◽  
Abdul Matin
Keyword(s):  
Gated Recurrent Unit ◽  
News Headlines

Talking, Reading, and Writing on Smartphones

2020 ◽  
pp. 407-422
Author(s):  
Naomi S. Baron
Keyword(s):  
Mobile Phones ◽  
Easy Access ◽  
Reading Habits ◽  
Internet Protocols ◽  
Reading And Writing ◽  
Reading Material ◽  
Voice Calls ◽  
News Headlines ◽  
Communication Preference

Mobile phones have increasingly been transformed from speaking technologies to devices for reading and writing. Cost helped drive this shift since written short messages were historically less expensive than voice calls. A second factor was communication preference for texting over talking, especially among younger users. With ready Internet access on smartphones, reading habits began shifting as well. Social networking messages, along with other short texts such as weather reports or news headlines, made for obvious reading material, as did the plethora of longer written documents available online. The e-book revolution enabled readers to retrieve entire books on their phones. Mobile phones are also writing platforms. Developments in hardware and software dramatically simplified the input process. Instead of multi-taps, users now rely on virtual keyboards for easy access not only to alphanumeric characters and punctuation marks but also to sophisticated predictive texting and autocorrection. Interestingly, while technically we are writing when inputting text on smartphones, many users do not perceive such input as real “writing”—a term they reserve for writing by hand or with a computer. Additional writing issues include norms regarding so-called textisms, along with the role of culture in shaping attitudes regarding linguistic correctness. Many organizations are discontinuing voicemail systems in favor of written messaging. At the same time, voice over Internet protocols continue to grow, and small voice-activated social robots designed for home use are proliferating. The chapter closes by asking what the spoken–written balance on smartphones might look like in the future.

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

scholarly journals Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3059
Author(s):  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Seung-Bae Jeon ◽  
Minkyo Youm
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Moving Average ◽  
Computational Cost ◽  
Nonlinear Problems ◽  
Transportation Systems ◽  
Mobility Data ◽  
Challenges And Opportunities ◽  
Moving Average Model ◽  
Gated Recurrent Unit

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

IoT-based Epidemic Monitoring via Improved Gated Recurrent Unit Model

2021 ◽  
pp. 1-1
Author(s):  
Fang Hu ◽  
Jia Liu ◽  
Liuhuan Li ◽  
Mingfang Huang ◽  
Changguo Yang
Keyword(s):  
Epidemic Monitoring ◽  
Unit Model ◽  
Gated Recurrent Unit

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

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